Unlike methods that explain the emerging patterns in the learned weights or activations [1]}, [2]}, [3]}, attribution techniques are task-specific and can only be applied to the inference pipeline for which they have been developed. Therefore, none of the mentioned attribution methods can be directly applied to new tasks, such as object detection.
Inspired by the RISE [4]} method of generating saliency maps to explain visual classifiers, we propose D-RISE, a method to produce saliency maps for object detectors that does not rely on gradients or the inner workings of the underlying object detector. The advantage of this approach is that our method can be in principle applied to any object detector. Extending methods that produce saliency-based visual explanations to object detectors is non-trivial as this task requires explanations for each object instance as opposed to a global explanation for each image. Moreover, explanations must take into account both class predictions and bounding box predictions.
